| Ken Raj: |
00:05 |
There’s some people where their DNA profile shows them to be actually older or younger than they are, so their DNA methylation age is either older or younger than their chronological age. |
| Kendall Kendrick: |
|
HumanOS. Learn, master, achieve. |
| Dan Pardi: |
00:19 |
Welcome back, everyone. This is Dan Pardi, and today I have with me, Dr. Ken Raj. Ken is a senior scientific group leader at Public Health London. He has worked extensively with Dr. Steve Horvath of UCLA, developing and interpreting genomic biomarkers of aging including, most famously, the epigenetic clock, which we’ll discuss today. So, without further ado, Ken, welcome to the show. |
| Ken Raj: |
|
Hello, hi. |
| Dan Pardi: |
00:33 |
For our audience less familiar with some of the terms, what is the difference between genetics, genomics, and epigenetics? |
| Ken Raj: |
|
Genetics would be things which are associated, or deal with, the sequence of the DNA, the ACGT’s that make up the sequence. And that can be things that include things such as mutations, for example, where the sequence of the DNA’s change. Epigenetics, put simply, it encompasses several things, but the common feature between those several things is that they do not actually involve the change to DNA sequences. So the most common ones you will find, as part of epigenetics, is a methylation on some residues on DNA, on nucleotides, such as cytosines. So methylation of cytosine’s a very common modification that does not change the base, but it can have a profound effect on the expression of genes from the region or slightly away from the region where the methylation occurs. |
| Ken Raj: |
00:56 |
There are other elements of epigenetics, such as modifications on histones, proteins that DNA wrap around, and people have also included things now, such as small RNA, microRNA, RNA that do not code for proteins, but can regulate gene expression. So these are things that come on the cluster of epigenetics. Basically, changes that control or regulate gene expression that does not involve the mutation of the sequence of DNA. |
| Dan Pardi: |
|
When I was first learning about it, the prefix, epi-, was helpful to meet my understanding. So epi- means above. So, above the genes, which I thought, okay this is not the genes themselves, but it’s effecting what kind of proteins, ultimately, are produced. |
| Dan Pardi: |
00:57 |
Wonderful. So, we talked about some of the mechanism there involved. It’s interesting we just Tweeted that men whose fathers smoked at the time of pregnancy, have a 50 percent lower sperm count compared to men with none smoking fathers. And this finding was independent of the mothers’ exposure to nicotine. And, so, this speaks to the heritability element of epigenetics. So these things were not a part of our genes, seem to be able to be passed down. What do we know about that? |
| Ken Raj: |
|
It is somewhat surprising on one hand, because, it is known that DNA methylation, for example, the one that’s most common, is often or largely erased in germ cells and that it is not largely preserved. But, on the other hand, there are areas, there such [inaudible 00:03:26] where the DNA rest, is epigenetic changes are preserved, and they do actively get on to the next generation. |
| Ken Raj: |
01:06 |
Now, how this happens, and why this happens on the selection, and, which region where the epigenetic markers are maintained, is still not very well known. It is just, at this stage, we can only describe it and say it happens. But, it doesn’t happen to all aspects or all regions of epigenetic markers, but it happened to a selection of epigenetic markers that actually gets inherited to the next generation. |
| Dan Pardi: |
|
So, some sort of exposure within a lifetime can then be passed down effecting the regulation of genes and subsequent generations. And, do we know how many generations has that been observed, travel? |
| Ken Raj: |
02:00 |
I, personally, do not know that. And, I think you will need a very large number to do that study because of the segregation and the diluent that is [inaudible 00:04:17]. But, in theory, if you get it there, you would think that, if you can pass it on from one generation, you would think that it might quite easily move onto the next generation. But, I have to say, I’m treading on very dangerous ground too, because I have good [inaudible 00:04:31] that support that. I’m just making this opposition here. |
| Dan Pardi: |
|
Right. Let’s turn to your work with Steve Horvath, who’s a researcher at UCLA. You guys have done a lot of work on something called the epigenetic clock. So, what is the epigenetic clock and what exactly is this clock measuring? |
| Ken Raj: |
02:29 |
Okay. So I must say, at this point, that my collaboration with Steve is very productive and nice. And this whole thing started with Steve, himself, and I’m here somewhat representing him. Steve is a mathematician, and what Steve did was to question whether the observed changes of DNA methylation, as we age, can be used to correlate with our age. |
| Ken Raj: |
|
What has been known for sometime is, as we age our DNA methylation profile, so that means there’s some sites become more maturated, some parts of the DNA become less maturated, but generally there’s an epigenetic drift. Now, a drift tells you something changes, but it doesn’t tell you that it changes to any specific degree. And, during years it was not thought that there was any specificity, or any tie and resolution of the change in function of age. |
| Ken Raj: |
02:43 |
Now, what Steve did then, was to take data that is already available, of DNA methylation profiles, that’s freely available, that could be correlated to the age of the DNA profiles from which it was obtained. So, they’re talking about thousands of profiles with people of known age. And, then he used machine learning methods to look at these profile effectively, to put it simply, to almost do a face recognition type, but here we’re looking at DNA methylation profiles of each of this. |
| Ken Raj: |
|
And the machine just looks through this and learns to identify, or correlate profiles to age. And, by doing so and with his mathematical wizardry, he managed to come up with an algorithm that can take a DNA methylation profile of cells, and correlate that, and say that particular profile would suggest that the person is of a particular age. And, that is something that was surprising because we did not think, until then, that the epigenetic drift has such high specificity in its correlation with chronological age. |
| Dan Pardi: |
03:10 |
Wasn’t it questioned at first, because of the correlation was so high that people were almost in disbelief? |
| Ken Raj: |
|
It was questioned at first, it’s one of these strange things where your data is too good to be true. It was questioned first, and it still is questioned today. But those uninitiated, when it is brought up, it is almost in disbelief that you could have the precision of age prediction to that degree. |
| Ken Raj: |
03:32 |
But, it does. It does really have that high degree of age prediction. It is plus or minus about three years. |
| Dan Pardi: |
|
What is it, about 350 sites that this algorithm and neural network is looking at in it’s calculation to predict age? |
| Ken Raj: |
03:56 |
Correct. Initially, the data that was presented to the machine, was data of at least 27,000 CPG’s, as we can refer to them. So, from this 27,000 many of them were related to age. They are changes related to age. But, from these, the learning machine was able to pick up the ones that were most predictable age, and this amounted to about 353 CPG’s of which not all of them are changing in one direction. Some of these CPG’s increase methylation and function of age. Some of these CPG’s decrease methylation and function of age. |
| Dan Pardi: |
|
Some people are unfamiliar with CPG’s. What is that looking at? |
| Ken Raj: |
04:08 |
Okay, the CPG’s are, in fact a very simple thing, it is that we have ACGT’s in our DNA sequence, and any C’s that come before a G is referred to as a CPG. The P refers to the phosphate bonds that link the cytosines to the guanines. And it is the C’s that are methylated, not the G’s. So when we talk about CPG methylation, we refer specifically only to the methylation of the cytosines. |
| Dan Pardi: |
|
Are the methylation changes, that are being recorded, playing a direct role in the aging process, do we know if they’re driving the process? |
| Ken Raj: |
04:35 |
That, I think, is the question we are working on now. We do not know if they’re a consequence of change that is related to age or if they are driving the change that causes one to age, right? So, is it a passenger or the driver? It is a question that has yet to be answered. |
| Dan Pardi: |
|
Let’s talk about the difference between chronological age and biological age. So what is the difference there? |
| Ken Raj: |
04:48 |
So, when Steve got this algorithm going, he put it to test anything that he could get hands on DNA profiles which are age related, to see how accurate this algorithm is. For most of the time, the DNA methylation age which is used to define the prediction of age by the algorithm, which is called an DNA Methylation Age, most of the time the DNA methylation age corresponds very well with the actual age of the person which we refer to as chronological age. You can look at a fossil and know how old you are. So that’s a chronological age. |
| Ken Raj: |
|
But there are outliers, which are really, really interesting. Now, there’s some people where their DNA profile shows them to be actually older or younger than they are. So their DNA methylation age is either older or younger than their chronological age. Now, we could say that, that could be due to errors that maybe the method is not very good. Or it could be due to a mistake in DNA collection or something else. |
| Ken Raj: |
05:13 |
Now, this could very well be the case, but it turned out that it is not. It has actually got greater meaning than that. How did he know that? Because, when he looked at the cohorts who experience, or who are suffering from, or who have pathologies, or have conditions which are related through aging, for example, people who are suffering from Alzheimer’s, Parkinson’s, what he found was people who suffer from these pathologies have DNA methylation age that is greater than their chronological age. |
| Ken Raj: |
|
So when you see that, you then that the discrepancy that you observe is not a mistake, that it does have a correlation to a phenotype of aging. So then we say, if this person that, say, is 50 years old but his DNA methylation age is 60 years old, he’s aging faster than his chronological age. So we say, then, his biological age is 60 even though his passport will tell us that he’s 50. |
| Dan Pardi: |
05:43 |
Right. And vice versa. So you can see people that are younger, compared to their chronological age. So their biological age might be 40 even though they’re 50, or as you said, their biological age could be 60 even though they’re 50. And I imagine then, trying to understand, what are those factors for both phenotypes will be really helpful, and us understanding how to intervene. And so that’s what you’re looking into now? |
| Ken Raj: |
|
Correct. Yes. |
| Dan Pardi: |
06:18 |
You’ve also been looking into life expectancy. So if you can look at these markers of biological age, does that correlate with life expectancy in an individual? And do you see any discrepancies between somebody who might be 50 years old, but their biological age looks like they’re 60, but they still maintain health, it doesn’t quite match up in terms of other phenotypic elements of how they’re showing up? |
| Ken Raj: |
|
So, firstly, it’s important to remember that when we do these sort of studies, and Steve does these sort of studies, it is not possible to take it down to an individual just yet. We can’t say that an individual will definitely demonstrate a particular phenotype if it has got a biological that is greater than his chronological age. We are looking at the probability that this person would develop the certain pathology. Okay, so, it’s not a certainty. Because, most things in biology anyways, is on probabilities. The probability of getting cancer is not a certainty of getting cancer. So on and so forth. |
| Dan Pardi: |
07:00 |
Right. |
| Ken Raj: |
|
Whether there will be people who will have greater biological age and still be healthy, well, I think you could put a [inaudible 00:12:52] on that. There will be people like that. That does not prove one way or another that this is right or wrong. It would take maybe a thousand people, then you will see the majority of them who have greater biological age will then present with a certain pathology. So that is definitely clear. |
| Ken Raj: |
07:06 |
Now, I can’t remember the first question you asked me. |
| Dan Pardi: |
|
Life expectancy and correlation of the biological clock. |
| Ken Raj: |
07:24 |
Oh, right. Now, strangely this is a very fascinating one. The short answer is yes, you can actually predict life expectancy. More precisely, Steve uses the term, which is time to death. Now, although this particular algorithm, which we refer to as epigenetic clock, this 353 CPG’s can predict that. Steve has come up with better clocks, better algorithms, that predict time to death. Even to a far greater accuracy than this one. This one can already do it, but he has come up with better ones that can predict that. |
| Ken Raj: |
|
You can predict it’s one thing, but what’s more useful is to correlate things to life expectancy, and what is correlated is, if you have biological age that is younger than your chronological age, that correlates with longer life than if you have greater biological age compared to chronological age. Which, I think, makes a lot of sense. |
| Ken Raj: |
07:29 |
And, what is also important, is that the things that we associated with good aging, such as good diet, exercise, these things are also correlated with younger biological age. Basic lifestyle that’s bad for you, is correlated with greater biological age. And if I may just add this point, that if you read Steve’s paper, you will find a phrase that is used often, which is called age-acceleration, acceleration of epigenetic age. And acceleration, if it’s positive, that means that person is aging biologically faster than his chronological age. And, if age acceleration is negative, then he’s aging biologically slower than his chronological age. And, if it’s zero, he’s aging as you would expect from his chronological age. |
| Dan Pardi: |
|
There are commercially available epigenetic test. I’ve done one through Osiris Green, which was created by Neil Copes, whose been a guest on the show talking about beta-Hydroxybutyrate and it’s longevity properties. From what you’ve seen, how tight can we get this age prediction down to? |
| Ken Raj: |
07:38 |
There are many different methods that people have come up with, other than DNA methylation, but so far none is closer to the actual number than DNA methylation. And so, in terms of method, it is still the best method. Now, in terms of algorithms, there are several people that have come up with their own epigenetic clocks, their epigenetic measures of age, and they vary in their accuracy and in their range, how far they can go. And so far, I think, that is as close as you can get to, if I’m not mistaken, is about three years. Two to three years. |
| Ken Raj: |
|
I stand corrected is someone comes and points out the data on another I missed out, but, I think it wouldn’t be too far from that. |
| Dan Pardi: |
08:16 |
If somebody were to get this test done, at what speed can you see changes in epigenetic change take place? So, lets say somebody gets one of these tests done, that motivates them to start doing some things to live a healthier lifestyle. Would they wait five years to get tested again to have visibility, if they’ve had an impact? |
| Ken Raj: |
|
The short answer is, no we do not know. You can imagine to do that, you do need quite a bit of studies. Usually, you need quite a bit of people recruited to do that. Because, again, in biology, we’re always looking at the average, and just a handful wouldn’t give you any reliable answer to your question. |
| Ken Raj: |
08:20 |
So, this has not been done yet, at least not prospectively. Now, retrospectively, maybe some data can be fished out from databases and be looked at, but not to my knowledge, no. |
| Dan Pardi: |
|
Now, speaking of things that can affect this, certain lifestyle factors, like diet, exercise, they do affect the clock that you aluded to. What are some of the things that have been researched to show an impact in epigenetic aging? |
| Ken Raj: |
08:49 |
Okay. Diet is one. So, you can also suspect, and your suspicion would be right, that those things which are good for you, things like vegetables, would be associated with a negative age acceleration. And things which are bad for you, like a lot of red meat, and things like that, would be associated with positive age acceleration. You won’t find many things to be a surprise. What you would expect would largely turn out to be correct. Things which are good, will turn out to be negative age acceleration, and then things which are bad would be the other way around. |
| Dan Pardi: |
|
It seems like cumulative life stressors seem to have an impact here. So, even things like socioeconomic factors, education level, things that might correlate with psychological stress, high vegetable intake and fish, completing obesity exercising seem to be good. But like you said, it probably won’t be that much of a surprise. |
| Ken Raj: |
08:57 |
On one hand, it’s unfortunate, because we like surprises. It will be great if you find something unexpected. But, on the other hand, it somewhat comforting that this method which was identified, using machine learning, can come up with the same conclusions in regards to what is good for us that matches the conclusion that were derived from other means, from clinical means. So, it’s a pretty good validation of what it is actually seeing. |
| Dan Pardi: |
|
Speaking of cumulative life stress, Dr. Horvath has suggested that that epigenetic clock is measuring cumulative work done by this epigenetic maintenance system to maintain the genetic stability. So, is this having a protective role in things like cancer? |
| Ken Raj: |
09:13 |
That’s one of the things that Steve has proposed. Steve has a model, whereby, he thinks that perhaps there is a epigenetic maintenance system, called EMS, it has not been characterized, if it exists it has not yet been characterized. But, for what it’s worth, if there’s EMS to maintain the DNA methylation [inaudible 00:18:44] in a cell, he believes that if a cell is stressed, this EMS will go into overdrive to try to maintain epigenetic stability. This overdrive activates itself, or manifests itself, as increase epigenetic aging. That’s one way to see it. |
| Ken Raj: |
|
Now, I have to stress that Steve himself would agree that this is a proposition, it has not yet had any clear empirical backing to that. But, it’s an interesting hypothesis. |
| Dan Pardi: |
09:18 |
If epigenetic markers are reflecting repair to DNA damage, then things that might influence that could actually interfere with things like cancer. So the analogy, what’s senescents in my mind, you don’t really want to prevent senescents, but you want to clear it after it’s occurred. Here, you don’t want to necessarily effect those epigenetic factors that are reflecting DNA damage repair, but maybe there’s a way to intervene circuitously, so then make that process age slower. |
| Dan Pardi: |
|
So, how do you, then, induce less DNA damage? That’s probably the main thing we’re after here. |
| Ken Raj: |
09:52 |
That’s what Steve was getting at, it’s a possibility. And, it’s interesting that you mention senescents, for example, because one of the things that will immediately pop up in most peoples minds, is epigenetic aging that we’re talking about, is that a measure of senescent cells in the body? Because, it would make sense if it does, because senescent cells reportedly accumulate in our body and function of age and would be DNA methylation profiles that correlate with age that we call epigenetic aging, would it be a profile that is derived from the increased number of senescent cells in the body? |
| Ken Raj: |
|
So this is one of the things that we thought, initially, to be the case. But, when we did our work, the results were quite clear that although senescent cells increase in number in function of age, the DNA methylation aging algorithm do not actually measure senescent cells. So it is a distinct form, or a distinct root, of aging from that of senescent cells. |
| Dan Pardi: |
10:19 |
So with the work in synalytics and synamorphics, if you can clear them, perhaps that could be something that might affect the rate of epigenetic aging if inflammation is part of the case of accelerated aging. |
| Ken Raj: |
|
From that root, it’s possible. Yes, that can be the case. What’s clear is that if we have a dish of cells that are prevented from getting into synapsins, for example, if you can stop them from getting into replicative synapsins by expressing telomeres, the cells will continue to grow and become immortal. All right? And yet, in function of time, these cells continue to age. Which is basically the answer to the question, which is, what is the relationship that varies one between synapsin and epigenetic aging. And here we can see that even in the absence of replicative synapsins, epigenetic aging continues to increase in function of time. |
| Dan Pardi: |
10:53 |
How about drugs? The major classes of epigenetic drugs that are currently in use are DNA methylation inhibitors, bromodomain inhibitors, histone acetyltransferase inhibitors, there’s a variety of things that can affect this. Are any of those being investigated currently to modify this clock? |
| Ken Raj: |
|
It is, in fact, being done in my incubator right now. So we don’t have the answer to that yet. One of the things that maybe worth mentioning is that, what Steve has managed to do is actually carry a research from the last common end. Which is that he is able to have a map that looks at the end point, which is the human body itself, and demonstrate age. And, he’ll be moving backwards towards the cells in the dish, and to try and establish assays of aging in the dish, so that then we can do all these tests that you were talking about. |
| Ken Raj: |
11:24 |
Because, most other discoveries are done the other way around, where we do things in vitro and then we try to get it in vivo. You got it from the organism and then you try to move it back to the plate. Why? Because, you can’t do all these tests that you said, that all this compounds by feeding people with it, obviously. So, we have to feed cells with it. And that is why, since his discovery of this algorithm, together with him I have been working to establish an in vitro model of aging in the laboratory, so that we can test all these wonderful drugs and inhibitors you were talking about to interrogate their effect on epigenetic aging. |
| Dan Pardi: |
|
Seems very promising to me, if you just over express H3 and H4, you could extend life span. To me, makes sense. If you lose histones, or if you lose nucleosomes, and the DNA is then more accessible to DNA damaging agents, then you could more aberrant transcription. And it just seems like a vicious cycle. As I alluded to earlier, the Ketogenic diet is becoming popular within anti-aging science base, beta-Hydroxybutyrate acts as a HVAC inhibitor. |
| Dan Pardi: |
11:45 |
You know, Copes work showed that it promoted life extension in C. elegans. [Eric Fardin 00:23:30] and John Newman, and others that [inaudible 00:23:34], and UCSF, they’re [inaudible 00:23:36] looking at butyrates and their properties as signaling molecules to effect epigenetics. I’m excited about that, because that is a lifestyle intervention we can do now. |
| Ken Raj: |
|
Absolutely. |
| Dan Pardi: |
11:46 |
So we talked about epigenetic age biological age. What are some other uses of this technology that humans can benefit from? |
| Ken Raj: |
|
Okay, I think the obvious one we cannot run away from is that, if we can find compounds that mitigate epigenetic aging, that would be one potential. I’m a bit uncomfortable talking about things like this, because we know the many promises made about many things that never worked out. But for one, it’s worked. |
| Ken Raj: |
12:10 |
I think it’s quite obvious that, barring complications that we cannot foresee, if we can find compounds that either slow down or stop epigenetic aging, now that may not be possible, but if we do that work and perhaps move towards a healthy aging, it will not stop you from aging all together. But it can help you to have better aging where you will be healthier. |
| Ken Raj: |
|
Because, remember that epigenetic aging is only one of the roots that lead to the final aging phenotype, because there is still the senescents mediating aging. That certainly if we slow down one, it would only be good if we could do that, barring unexpected consequences that we cannot at the moment predict. So, that would be one aspect. |
| Ken Raj: |
12:45 |
And the other aspect would be also, apart from drugs or compounds, it would be finding out things within our natural diet. What other things that can be helpful in slowing down epigenetic aging. That would be nice. Potentially, if better systems are developed, we can also look at lifestyle and try to understand what aspect of life, even though we know some aspects like exercises and things like that can contribute, but what is it about exercise that contributes. Is it exertion? It is greater intake of oxygen? What actually about exercise that helps? |
| Ken Raj: |
|
So now that we have a way to measure the rate of aging, this really changes the game, because prior to this, how would you test whether something has an effect on age. We can’t just take people and get them something and hope that within a year you will see a difference, because you might not see a difference within a year, you might have to wait ten years. And that kind of research is really difficult. |
| Ken Raj: |
12:45 |
But with this objective, we are measuring aging, we can actually shorten testing regimes that we can now start to do very empirical and well controlled experiments either in the laboratory or even using animal models to try to pin down what are the elements in drugs, in diet, in lifestyle, that can actually help slow down epigenetic aging. |
| Dan Pardi: |
|
Diagnosing age related conditions could also be one of the possible utilities here. You can identify pathology earlier, then you can treat it, usually, more effectively. I’ll be interested to see how this identification of somebodies biological age could potentially lead to better mitigating therapies for higher likelihood of having variety of age related diseases, cancer, diabetes, etc. |
| Ken Raj: |
13:08 |
That was actually one of the first things that came to mind. The reason I never brought that up as one of my answers, is that it is a double edge sword, isn’t it? What you said was right. That they are the noble end of those things, that we say that if we can predict, we can perhaps be ready and try to mitigate it by other means. But you can see that it can also be an issue in terms of insurance, for example. The users that are not necessarily so useful, or helpful, for humans. So that may be something that would have to be dealt with in a more holistic way to look at it from the ethical aspect as well. |
| Dan Pardi: |
|
Yeah. You alluded to some of the things you’re working on now. What are some of the big areas related to this work that you’re going to be investigating in the coming months to years? |
| Ken Raj: |
13:10 |
In terms of the pure science question, I definitely want to know what is this epigenetic aging? It’s predicting powers is no longer in doubt, it’s potential use is not longer in doubt, but we really need to get to the mechanism. What determines the ticking rate? The ticking rate I refer to is the methylation or the demethylation of CPG’s that are related to age. What causes them to change? |
| Ken Raj: |
|
And of course, that gets into the question you asked me in the beginning. Are these changes merely passengers? Or are they the real drivers of aging? This is a question that is so difficult to answer, because they’re separate in cost so the effect is not the easiest thing to do sometimes. If you’ve got a right model, you can do it very easily. But, if not, you keep going around and around in circles. |
| Ken Raj: |
13:12 |
But, that is certainly, to me, the biggest question to ask. If this epigenetic aging is going to feature very prominently in the future of our health processes, we really need to get down to the bottom of the mechanism. What are we looking at. We want to know what we are measuring, not just sufficient to know it’s very accurate, but what really drives it. So that really is number one. |
| Dan Pardi: |
|
It would be very interesting to also know, is it simply reflective of aging, or is it a cause of it? And does that change? So, when you reach a certain point, then it also is positive. [crosstalk 00:28:46] reflective. |
| Dan Pardi: |
13:45 |
Should young children have an epigenetic age done so that we understand, for instance, their aging relative to the burden of methylation that passed down to that generation. Are young children, perhaps, older than their counterparts based off of previous exposures of other generations? |
| Ken Raj: |
|
Hmm. That’s an interesting one. Of course it can be done. It will be quite, again, a very large cohort and a very good cohort. A very good quality cohort where we know what people are exposed to. It will be a very long term study, because you want to compare it to the actual chronological age. |
| Ken Raj: |
14:06 |
The answer is always, yes, it can be done. Has it been done at the moment? No. But we know one thing, there is limited number of long [inaudible 00:29:32] studies that have looked at something like this, not exactly what you’ve asked, like look at something like this and what we find is that, for what it’s worth, if a person exhibits age acceleration at one point in their life, and if you take this person and analyze the DNA and, lets say, ten years down the road, that persons DNA will also continue to exhibit age acceleration. |
| Ken Raj: |
|
So the rate of aging seems to be quite fixed. It does not mean it cannot be changed if you do something to it. That does not mean that. But it means that it’s rate, if left unperturbed, will remain as it is. So if you age faster, you will continue to age faster all around. If you ago slow, you will continue to age slow all around. That appears to be the case so far with the limited number long [inaudible 00:30:17] studies that have been carried out. |
| Dan Pardi: |
14:55 |
From a behavior perspective, these tests are now in the marketplace, if somebody gets back a biological age that is older than their chronological age, perhaps that accelerated aging was something that, that person was born with, or occurred earlier in life… |
| Ken Raj: |
|
Yeah. |
| Dan Pardi: |
15:12 |
For whatever reason. And if somebodies trying to do everything well now, they get that piece of data, and they say, “Well, I need to change everything up because it’s not working.” But maybe they’re actually doing as much as they could from a behavioral perspective, from a lifestyle prospective, to mitigate that accelerated aging. |
| Dan Pardi: |
|
There are so many questions, but, also some caveats for those who are looking into the number now. What will be the sequence of how frequently you should get an epigenetic test done, if you’re somebody who wants to look into that information to access if how you’re living is acting propitious to your epigenetic mechanisms. |
| Ken Raj: |
15:45 |
The short answer is, I don’t know. I don’t think anybody knows. At this moment, I don’t think anybody knows. |
| Dan Pardi: |
|
Right. And this is so interesting. |
| Dan Pardi: |
15:52 |
So my last question for you is, will Steve Horvath win a Nobel Prize for his work here? You’re a betting man, is it over 50 percent? |
| Ken Raj: |
|
I think he should. Like you, I think he should. His contribution will certainly be significant in the coming years. And it’s been five years since he discovered this. Now, I know papers don’t mean anything if it doesn’t impact human life, but the papers that come out from his publications are very close to human life. It’s not the kind of papers that talk to us about how something works in the cells, that is far removed from human health, but a lot of papers that come out from that base on his discovery are really dealing with actual day to day health, human life. |
| Ken Raj: |
16:14 |
So, I think this contribution will see the light of day in a way that it deserves. And when that happens, I think, that’s probably going to come. |
| Dan Pardi: |
|
This is so fascinating. Everything in the aging space now has captivated my mind, and I’m trying to the best I can for my audience to translate why this is so darn interesting. And if you do care about your health, because hopefully within a lifetime, this will be something that will have increasing meaning to be a biomarker that we can actually upon. |
| Ken Raj: |
16:29 |
I hope so. |
| Dan Pardi: |
|
So thank you again for your time and all the work you’re doing. |
| Ken Raj: |
16:40 |
Thank you. |
| Kendall Kendrick: |
|
Thanks for listening and come visit us soon at, humanOS.me. |
Comments
Post a Comment